MLS-C01 Modeling Practice Question
Which THREE of the following are valid metrics for evaluating a regression model?
⚠ Common exam trap
A common mix-up: candidates confuse classification metrics (F1 score, Accuracy) with regression metrics, especially when the question asks for 'valid metrics' without specifying the model type, leading them to select metrics they are more familiar with from classification tasks.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
R-squared (R²)
R-squared (R²) is a valid regression metric that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, and is commonly used alongside other error metrics to assess model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
R-squared (R²)
Why this is correct
R-squared is a common regression metric.
- ✗
F1 score
Why it's wrong here
F1 score is for classification.
- ✓
Mean Absolute Error (MAE)
Why this is correct
MAE is a standard regression metric.
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE is a standard regression metric.
- ✗
Accuracy
Why it's wrong here
Accuracy is for classification.
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